Bibliographic record
Abstract
One of the major actions to reduce the Greenhouse Gas (GHG) emissions that are the leading cause of the global warming problem, is the development of Electric Vehicles (EVs). Thus, the application of EVs in the routing problem for delivery in short-haul distances is investigated in this dissertation. A routing problem involving EVs is defined on a set of vertices, including a start depot, a set of customers, charging stations, and an end depot. Starting from the depot, one of the main goals of this problem is to minimize transportation costs by visiting all the customers before arriving at the end depot. In Chapter 2, a routing problem with a mixed fleet of vehicles and different charging technologies (including Level 1, 2, 3 chargers, and battery swapping) is considered. A Mixedinteger Linear Programming model is then developed and solved by exact, and metaheuristic solution approaches. The results illustrate that EVs are more likely to be used than Conventional Vehicles (CVs) in the last-mile delivery problems. Also, Level 3 chargers may be the first choice for end-route charging in these problems. In Chapter 3, reducing the total GHG emissions for CVs is thesecond objectiveinaddition to minimizingthetransportationcosts.Tosolvethis bi-objective model, three multi-objective solution methods (i.e., weighted-sum, Œµ-constraint, and hybrid methods) are integrated with the Adaptive Large Neighborhood Search. The effects of the service area, the density of the stations, and charging power on the routing problem are investigated in the third chapter. The trade-off analysis reveals that by marginally increasing transportation costs, GHG emissions can be reduced considerably. Finally, in Chapter 4, a new robust model for the Electric Vehicle Routing Problem (EVRP) is introduced to handle the energy consumption uncertainty of EVs. Moreover, the on-time delivery factor that results in customers’ satisfaction is addressed by minimizing the delay and the earliness during distribution. The effects of uncertainty levels for energy consumption on the routing problem are analyzed by performing a Monte Carlo simulation. The trade-off analysis indicates that the on-time delivery can be improved by 11% by increasing 2.5% of the costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".